High expression of transcription factor EGR1 is associated with postoperative muscle atrophy in patients with knee osteoarthritis undergoing total knee arthroplasty

High expression of transcription factor EGR1 is associated with postoperative muscle atrophy in patients with knee osteoarthritis undergoing total knee arthroplasty | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article High expression of transcription factor EGR1 is associated with postoperative muscle atrophy in patients with knee osteoarthritis undergoing total knee arthroplasty Xiao-yang Liu, Qiu-ping Yu, Si-qin Guo, Xu-ming Chen, Wei-Nan Zeng, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4839822/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 01 Oct, 2024 Read the published version in Journal of Orthopaedic Surgery and Research → Version 1 posted 11 You are reading this latest preprint version Abstract Background: Muscle atrophy is a typical affliction in patients affected by knee Osteoarthritis (KOA). This study aimed to examine the potential pathogenesis and biomarkers that coalesce to induce muscle atrophy, primarily through the utilization of bioinformatics analysis. Methods: Two distinct public datasets of osteoarthritis and muscle atrophy (GSE82107 and GSE205431) were subjected to differential gene expression analysis and gene set enrichment analysis (GSEA) to probe for common differentially expressed genes (DEGs) and conduct transcription factor (TF) enrichment analysis from such genes. Venn diagrams were used to identify the target TF, followed by the construction of a protein-protein interaction (PPI) network of the common DEGs governed by the target TF. Hub genes were determined through the CytoHubba plug-in whilst their biological functions were assessed using GSEA analysis in the GTEx database. To validate the study, reverse transcriptase real-time quantitative polymerase chain reaction (qRT-PCR), enzyme-linked immunosorbent assay (ELISA), and Flow Cytometry techniques were employed. Results: A total of 138 common DEGs of osteoarthritis and muscle atrophy were identified, with 16 TFs exhibiting notable expression patterns in both datasets. Venn diagram analysis identified early growth response gene-1 (EGR1) as the target TF, enriched in critical pathways such as epithelial mesenchymal transition, tumor necrosis factor-alpha signaling NF-κB, and inflammatory response. PPI analysis revealed five hub genes, including EGR1, FOS, FOSB, KLF2, and JUNB. The reliability of EGR1 was confirmed by validation testing, corroborating bioinformatics analysis trends. Conclusions: EGR1, FOS, FOSB, KLF2, and JUNB are intricately involved in muscle atrophy development. High EGR1 expression directly regulated these hub genes, significantly influencing postoperative muscle atrophy progression in KOA patients. Knee Osteoarthritis Total knee arthroplasty Muscle atrophy EGR1 Bioinformatic Analysis. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1 Introduction Muscle atrophy, characterized by the reduction in muscle mass and strength, frequently occurs as a complication after total knee arthroplasty (TKA) in patients with knee osteoarthritis (KOA) [ 1 , 2 ]. Although TKA has demonstrated significant improvements in pain, active function, and quality of life in patients [ 3 ], research has demonstrated a high incidence of postoperative muscle atrophy that can persist for many years, particularly presenting as quadricep weakness [ 4 ]. The quadriceps muscle plays a critical role in knee extension and overall lower extremity function, yet it is particularly vulnerable to atrophy following surgery. This weakness may lead to abnormal gait, lower physical capacity, and an increased likelihood of falls and other postoperative complications [ 5 , 6 ]. With more than 1.1 million TKA procedures performed in the United States alone in 2009 [ 7 , 8 ], muscle atrophy-induced mobility difficulties have emerged as a significant public health concern. The relationship between KOA and muscle atrophy is multifaceted and bidirectional. While muscle weakness and atrophy may catalyze KOA development [ 9 , 10 ], the presence of KOA can negatively impact muscle function and further exacerbate muscle atrophy [ 2 ]. Advanced imaging techniques have exhibited that the cross-sectional area of muscles in the affected limb of KOA patients is reduced by 19% compared to their normal limb [ 11 ]. Several clinical studies suggest that muscle atrophy is a result of a combination of surgical trauma, immobilization, disuse, and altered neuromuscular function [ 1 , 12 , 13 ]. Nevertheless, the underlying biological mechanisms concerning muscle atrophy in post-TKA patients are yet to be definitively determined. The primary objective of this study was to investigate the potential cellular and molecular mechanisms underlying muscle atrophy in patients with KOA following TKA. We adopted a bioinformatics approach and analyzed two datasets from the Gene Expression Omnibus (GEO) database, including the muscle atrophy and osteoarthritis datasets. Through comparative analysis, we identified common differentially expressed genes (DEGs) shared by both datasets and subjected them to transcription factor enrichment analysis. The enriched transcription factors (TFs) were intersected with the common DEGs to obtain the target TF most significantly expressed in both osteoarthritis and muscle atrophy. Subsequently, we leveraged protein-protein interaction (PPI) network analysis to identify potential hub genes and used gene set enrichment analysis (GSEA) to examine both DEGs and target TF for pathways associated with muscle atrophy. Ultimately, we validated the expression and function of the target TF leveraging reverse transcriptase real-time quantitative polymerase chain reaction (qRT-PCR), enzyme-linked immunosorbent assay (ELISA), and Flow Cytometry. This investigation adds to our understanding of the mechanism and diagnosis of muscle atrophy in post-TKA patients. 2 Materials and methods 2.1 Data Collection Two microarray datasets, GSE82107 and GSE205431, were downloaded from the GEO database ( http://www.ncbi.nlm.nih.gov/geo ) using "osteoarthritis" and "muscle atrophy" as keywords. The GSE82107 dataset, utilized in the GPL570 platform, accommodated synovial tissue samples from ten patients diagnosed with osteoarthritis and seven healthy controls. The GSE205431 dataset, employed in the GPL24676 platform, encapsulated RNA-sequencing data of skeletal muscle tissue samples from a total of twenty subjects suffering from end-stage osteoarthritis, including non-surgical limb samples (musculus vastus lateralis, n = 20) and surgical limb samples (vastus medialis, tensor fasciae latae, or gluteus maximus, n = 20). Moreover, count and TPM expression matrices of RNA sequencing data from 803 healthy human skeletal muscle tissue samples were downloaded from the GTEx database. 2.2 Analysis of DEGs The differential expression analysis was carried out using empirical Bayesian linear models within the "Limma" package of R software to detect DEGs in the osteoarthritis dataset (GSE82107). For the RNA-sequencing muscle atrophy dataset (GSE205431), the DESeq2 package, designed for data with a Poisson distribution, was utilized for differential expression analysis. The statistical significance threshold for DEGs was determined by adjusting the P -value (false discovery rate [FDR] 0.5. Volcano plots were used as a visual representation for all the DEGs. The DEGs common in both datasets were identified by a Venn diagram tool ( https://bioinfogp.cnb.csic.es/tools/venny/ ) and presented through heatmaps. 2.3 GSEA for the Two Microarray Datasets GSEA ranks the differential expression levels between different sample groups using pre-defined gene sets to determine if the gene sets are enriched at the top or bottom of the ranked list. The Hallmark gene set was used in this research for enrichment analysis. The ClusterProfiler (3.14) R package was used to analyze GSEA on the GSE82107 and GSE205431 datasets to determine significant functional and pathway differences based on differential expressional analysis results. The normalization enrichment score (NES) and FDR were calculated by setting random numbers to 1000. A gene set was considered to be significantly enriched when fulfilling the conditions NES ≥ 1.0, P <0.05, and FDR ≤ 0.25. 2.4 Analysis of TF Enrichment and Identification of Target TF CheA3 web ( https://maayanlab.cloud/chea3/ ) is a computational platform that enables the user to investigate the regulatory relationships between genes and transcription factors by entering a list of genes or selecting from predefined gene sets. It uses various algorithms and parameters to identify enriched transcription factors, gene ontology terms, and pathways. To enrich the corresponding transcription factors, the ChEA3 website was used to upload the common DEGs from the osteoarthritis and muscle atrophy datasets. The Venn diagram tool was utilized to obtain the intersection of enriched transcription factors with common DEGs to identify the TFs that exhibited significant differential expression across both datasets. Finally, the TF that could potentially regulate the most common DEGs was selected as the target TF. 2.5 Functional Annotation of Genes Regulated by Target TF The Clue gene ontology (GO) method, which combines statistical tests with visual representations, is an effective approach used in biological research to interpret large datasets and reveal functional relationships between genes underlying complex diseases. This method is frequently used in biological research to uncover functional relationships between genes and to gain insights into underlying biological processes in complex diseases. To explain the potential biological mechanisms of these genes, the common DEGs regulated by the target TF were extracted, and gene ontology biological process (GO: BP) functional enrichment analysis was carried out using the Clue GO plug-in in Cytoscape software. 2.6 PPI network construction and hub target TF-regulated genes identification A PPI refers to the process by which proteins form noncovalent bonds to create a protein complex. The construction of a PPI network facilitates the understanding of the molecular mechanisms of biological processes. The STRING database ( http://www.string-db.org/ ) is an online resource used for providing comprehensive information concerning PPI and functional associations among various species. Common DEGs that potentially regulate TFs were incorporated into the STRING database to scrutinize their interactions and produce a PPI network. Cytoscape (Version 3.7.1) is an open-source tool used for analyzing and visualizing molecular interaction networks, where the outcomes of the STRING database were imported. CytoHubba plug-in was applied to identify the top five hub genes according to their weight coefficients by using the Maximum Clique Centrality (MCC) algorithm. Finally, Cytoscape visualizes the five hub genes and their closely associated common DEGs. 2.7 Exploring the biological characteristics of target TF To investigate the biological function of the target TF, we selected 803 RNA sequencing data samples from the GTEx database that were derived from skeletal muscle tissue. We used the DESeq2 package to perform differential expression analysis with the median expression level of the target TF used as the grouping condition. Next, we performed the HALLMARK gene set enrichment analysis using the ClusterProfile package with screening conditions of P < 0.05 and FDR ≤ 0.25. 2.8 Experimental animals and grouping This experimental study received approval from the Animal Ethics Committee of Sichuan University. SD rats were primarily chosen for constructing the osteoarthritis model and conducting subsequent research. The study comprised three groups categorized by distinct expression levels of the early growth response gene-1 (EGR1): the normal group, the EGR1 overexpression group, and the EGR1 knockdown group. Rats in the normal group were obtained from the Experimental Animal Center of Sichuan University, while those in the EGR1 overexpression and knockdown groups were sourced from Cyagen Biosciences (Guangzhou) Inc. The experimental animals were individually housed in standard cages at the SPF Experimental Animal Center of Sichuan University (three rats per cage). They had ad libitum access to food and water under controlled temperature and humidity conditions and underwent a one-week acclimatization period within a 12-hour light and 12-hour dark cycle before the experiment. 2.9 Construction of the osteoarthritis model Following rat anesthesia, the skin on the medial side of the knee joint was aseptically cleansed, and a longitudinal incision approximately three centimeters in length was performed. The infrapatellar fold, connecting to the intercondylar fossa, was incised to reveal the underlying anterior cruciate ligament. Using microscissors, the anterior cruciate ligament was transected near the femur. The successful detachment of the anterior cruciate ligament was confirmed through an anterior drawer test. 2.10 Experimental cells The experimental cells were procured from Yagi Biotechnology (Shanghai, China) Inc, and genetic manipulation of the EGR1 gene was accomplished with lentiviral constructs. The cells were cultured in DMEM medium supplemented with 10% fetal bovine serum and 1% antibiotics (penicillin/streptomycin). They were maintained in a cell culture incubator under constant conditions of temperature (37°C) and humidity, with a 5% CO2 atmosphere. 2.11 qRT-PCR To validate the expression of target transcription factors, the extraction of total RNA from chondrocytes was done using the TRIzol reagent (Takara, Japan). The cDNA synthesis was performed with the use of the PrimeScript RT kit (Takara, Japan). The qRT-PCR was executed by following the instructions provided using the SYBR Green method (ES Science, China, QP002). The specific primer sequences used for the qRT-PCR with cDNA are presented in Table 1 . The calculation of mRNA levels for the targeted gene involved the 2-ΔΔCt method, which was then normalized to GAPDH. Table 1 The primer sequence for RT-qPCR amplification. Gene Forward Reverse EGR1 CCGAGCGAACAACCCTATGA AGGCTGAAAAGGGGTTCAGG FOS GCCTTCACCCTGCCTCTTC GCTCCATGTTGCTAATGTTCTTGA FOSB TGGGCCTTCAACTAGCACAAG GCTCCCTCCGACGGTTTCTG JUNB TACCTCCCACATGCACCACC CGCTTTCGCTCCACTTTGAT KLF2 GCGCTTTCGGTCTCTTCGAC GCAGTTGGTGTAGCTGCAAG GAPDH GACATCAAGAAGGTAATGAAGC GAAGGTGGAAGAGTGGGAGTT 2.12 ELISA The concentrations of tumor necrosis factor-α (TNF-α) and interleukin-1β (IL-1β) were detected following the instructions provided with the ELISA kit. Briefly, the reaction pore was sequentially filled with the standard, sample, antibody, and HRP-streptavidin, and the mixture was incubated at 37 ℃. Subsequently, the chromogenic solution and stop solution were added to the reaction pore in a sequential manner. The OD value was then measured using an enzyme labeling instrument, and the protein concentration was calculated based on the standard curve. 2.13 Flow Cytometry The apoptosis rate was determined using flow cytometry. Following cell collection, flow cytometry analysis was conducted using the Annexin V/FITC apoptosis detection kit I (BD Biosciences, Franklin Lake, NJ, USA) in accordance with the manufacturer's instructions. Annexin V single-positive cells were indicative of early apoptosis, propidium iodide (PI) single-positive cells were indicative of necrosis, and PI and Annexin V double-positive cells indicated late apoptosis. In order to assess skeletal muscle cell apoptosis in the context of osteoarthritis, skeletal muscle cells were incubated with 20 µ PE-labeled EGR1 (BD Bioscience) in the dark at room temperature. Subsequent analysis was performed using MODFitLT5.0 software (BD Biosciences). 2.14 Statistical Analysis All data were presented as the mean ± standard deviation (SD) assessed by the Shapiro-Wilk test for data normality. The Mann-Whitney U test was employed to compare two groups, while one-way analysis of variance (ANOVA) followed by the Bonferroni post-test was used for comparisons among multiple groups of samples. GraphPad Prism 9 software was utilized to plot and analyze the data. Statistical significance was denoted as * for P < 0.05, ** for P < 0.01, and *** for P < 0.001. 3 Results 3.1 Screening of DEGs Screening for DEGs was performed according to the study design flowchart presented in Fig. 1 . Using a cutoff criterion of an adjusted p-value of less than 0.05 and |logFC| > 0.5, a total of 1582 DEGs were identified from the GSE82107 dataset, of which 700 were upregulated and 882 were downregulated in the osteoarthritis group (Additional file 1) . In the GSE205431 dataset, 1229 DEGs were identified, of which 1169 were upregulated and 60 were downregulated (Additional file 1) . To validate the results, volcano plots ( Fig. 2 A ) were drawn. The DEGs from the two datasets were intersected by a Venn diagram, resulting in 138 common DEGs ( Fig. 2 B and Additional file 2) . Finally, the corresponding heat map is shown in Fig. 2 B. 3.2 GSEA of two microarray datasets The GSEA was performed on the microarray datasets (GSE82107 and GSE205431) to identify the gene sets that were significantly differentially expressed between the osteoarthritis and muscle atrophy groups compared to the control group. The Hallmark gene set was used as the predefined gene set. In the GSE82107 dataset, the gene sets involved in epithelial mesenchymal transition (EMT), TNF-α signaling via NF-κB, and inflammatory response pathways were significantly activated in the osteoarthritis groups ( Fig. 2 A ) . Similarly, in the GSE205431 dataset, the activation of EMT, TNF-α signaling via NF-κB, and inflammatory response pathways was also observed in the muscle atrophy group ( Fig. 2 A ) . These results suggest similar biological processes in osteoarthritis and muscle atrophy. Furthermore, osteoarthritis may cause muscle atrophy through the activation of these three shared pathways. 3.3 EGR1 is a target TF TFs play an essential role in regulating gene expression. To further explore the mechanism between osteoarthritis and muscle atrophy, we performed TF enrichment analysis on the 138 common DEGs. A total of 1632 TFs were enriched through the CheA3 transcription factor enrichment analysis website (Additional file 3) . The enriched TFs were then intersected with the common DEGs using a Venn diagram intersection to obtain 16 TFs with significant differential expression in both the GSE82107 and GSE205431 datasets ( Fig. 2 C and Table 2 ) . We finally selected EGR1 as the target TF due to the up to 90 common DEGs it regulates (Additional file 4) . The differential expression of EGR1 was visualized using a box plot ( Fig. 2 C ) . Table 2 Features of 16 Transcription factors. Gene Full name Regulation in GSE82107 Regulation in GSE205431 EGR1 Early growth response 1 UP UP FOS FOS proto-oncogene UP UP FOSB FOSB proto-oncogene UP UP JUNB JUNB proto-oncogene UP UP KLF2 KLF transcription factor 2 UP UP EGR2 Early growth response 2 UP UP SOX4 SRY-box transcription factor 4 UP UP NR4A2 Nuclear receptor subfamily 4 group A member 2 UP UP NANOG Nanog homeobox DOWN UP IRX5 Iroquois homeobox 5 UP UP MYCL MYCL proto-oncogene DOWN UP ZNF580 Zinc finger protein 580 UP UP ZNF428 Zinc finger protein 428 UP UP CIC Capicua transcriptional repressor UP UP TCF7 Transcription factor 7 DOWN UP ZBTB7B Zinc finger and BTB domain containing 7B DOWN UP 3.4 Functional annotation of EGR1 regulatory genes To verify the functions of the 90 common DEGs that can be regulated by EGR1, biological function annotation analysis was performed using the Clue GO plug-in in the Cytoscape software. The GO: BP enrichment results showed that the 90 common DEGs were involved in biological processes related to muscle atrophy ( Fig. 3 A ) . 3.5 Construction of PPI network and hub genes for EGR1 regulatory genes The 90 common DEGs from the previous analysis were used to screen for proteins interacting with them and to construct a PPI network using the STRING database ( Fig. 3 B ) . The results were imported into the Cytoscape software, and node connectivity was calculated using the MCC algorithm in the CytoHubba plug-in. The five top hub genes were EGR1, FOS, FOSB, KLF2, and JUNB genes, which were colored red as nodes in the PPI network ( Fig. 3 C ) . These genes have strong interactions, with darker colors indicating higher rank, pointing towards a possible pathophysiological mechanism linking osteoarthritis to muscle atrophy. 3.6 Biological characteristics of EGR1 The GSEA of 803 sequencing data from skeletal muscle tissues in the GTEx database indicated upregulation of EMT, TNF-α signaling via NF-κB, inflammatory response, Interferon-γ response, allograft rejection, and Kras signaling. In addition, high EGR1 expression was positively correlated with apoptosis, estrogen response late, coagulation, and apical junction pathways ( Fig. 4 A and 4 B ) . 3.7 Validation of EGR1 qRT-PCR was utilized to preliminary detect hub gene expression in osteoarthritis chondrocytes ( Fig. 5 ) . The results indicated that the expression of EGR1 significantly increased in the osteoarthritis group and the muscle atrophy group compared to the healthy control group under normal EGR1 expression conditions, with a significant statistical difference ( P < 0.01, Fig. 5 A). The expression levels of other genes related to EGR1, including Fos, FosB, KLF2, and JunB, were also significantly higher compared to the normal control group, with statistically significant differences ( P < 0.05, Fig. 5 A). By manipulating EGR1 through overexpression and knockdown, we found that when the EGR1 gene was overexpressed, the other four genes' expression levels significantly increased ( Fig. 5 B ) . Conversely, when the EGR1 gene was knocked down, the expression levels of the other four genes significantly decreased in response ( Fig. 5 C ) . These findings align with the results of bioinformatics analysis, suggesting that EGR1 functions as a transcription factor that regulates the expression of the other four genes, thereby impacting muscle atrophy in osteoarthritis. ELISA was employed to determine IL-1β and TNF-α expression levels in the synovial fluid of osteoarthritis patients ( Fig. 6 A-B ) . The results demonstrated that, under normal EGR1 expression conditions, the levels of IL-1β and TNF-α expression in the osteoarthritis group exhibited significant elevation compared to the control group. When the EGR1 gene was overexpressed or knocked down, the expression levels of IL-1β and TNF-α in osteoarthritis either increased or decreased significantly and these alterations were statistically significant ( P < 0.05). This finding demonstrates the significant regulatory role of the EGR1 gene in the inflammatory pathway. Additionally, the influence of EGR1 on apoptosis in skeletal muscle cells was extensively investigated through the implementation of flow cytometry analysis ( Fig. 7 A and 7 B ) . The obtained results revealed that manipulating the expression levels of the EGR1 gene in osteoarthritis chondrocytes led to a corresponding increase or decrease in the apoptotic rate of skeletal muscle cells, exhibiting statistically significant differences ( P < 0.05). This implies that the EGR1 gene plays a dual role in not only regulating the inflammatory response in arthritis but also influencing the process of apoptosis. 4 Discussion KOA is a prevalent musculoskeletal disorder that significantly impacts affected individuals through severe knee pain, stiffness, and dysfunction, ultimately leading to disability [ 14 ]. TKA is an effective intervention for advanced osteoarthritis that can improve patients’ pain and quality of life. However, postoperative muscle atrophy, mainly quadriceps atrophy, results in reduced muscle strength and mobility that diminishes the benefits of TKA [ 15 – 17 ]. Quadriceps weakness is a contributing factor to long-term postoperative disability assessment in KOA patients [ 18 , 19 ]. Patients with post-KOA are known to have muscle weakness and atrophy lasting several years [ 20 ]. Therefore, quadriceps weakness and atrophy concern clinicians treating KOA patients [ 21 ]. Previous research attributed quadriceps atrophy to disuse atrophy caused by joint pain [ 11 , 22 , 23 ], yet studies have proposed that it is a predisposing factor for developing KOA [ 24 ]. Moreover, surgery-induced skeletal muscle trauma is suggested as another potential cause of muscle atrophy [ 12 ]. However, the biological pathogenesis and underlying mechanisms to develop muscle atrophy in patients who undergo KOA surgery remain unclear. Our study aimed to identify critical genes involved in pathogenesis mechanisms underlying muscle atrophy. Two GEO datasets were analyzed using bioinformatics to screen for DEGs in both osteoarthritis and muscle atrophy groups systematically. The GSEA demonstrated that inflammatory-related pathways like EMT, TNF-α signaling via NF-κB, and inflammatory response were activated in both osteoarthritis and muscle atrophy disease states. We intersected DEGs in both datasets to obtain 138 common DEGs, which helped us identify genes jointly involved in these pathways. TF enrichment analysis was executed on the 138 common DEGs, and 16 transcription factors were found significantly enriched in osteoarthritis and muscle atrophy. Consequently, we identified EGR1, which regulates the most common DEGs, as the target transcription factor to investigate further pathways of muscle atrophy pathogenesis. EGR1 is a multifunctional and critical TF involved in various physiological processes, including cell growth, differentiation, and apoptosis [ 25 ]. EGR1 spans approximately 3.8 Kb containing two exons and an intron. It contains a highly-conserved DNA structural domain of three Cys2-His2 type zinc finger structures on chromosome 18 in mice and chromosome 5 in humans [ 26 – 28 ]. EGR1 is predominantly expressed in different connective tissues, such as tendons, cartilage, and bone, regulating extracellular matrix functions to facilitate tissue development, homeostasis, and healing processes [ 29 – 31 ]. Numerous studies have investigated EGR1's role in cartilage and bone, where it contributes to chronic diseases of articular cartilage degeneration [ 32 – 34 ]. EGR1 is highly expressed in synovial tissues, and articular cartilage of patients with osteoarthritis [ 35 , 36 ]. Under interleukin-1β (IL-1) stimulation, EGR1 recruitment to the Pparg promoter downregulates PPAR expression, restraining its protective role in osteoarthritis [ 37 ]. In patients with osteoarthritis, excess EGR1 was reported to increase proteins and transcripts of type I collagen in synovial fibroblasts [ 38 ]. While the effects of EGR1 on human skeletal muscle regulation remain unclear, previous studies highlighted the role of EGR1 in the promotion of differentiation of bovine skeletal muscle-derived satellite cells [ 25 ]. To examine the mechanisms of EGR1-mediated gene regulation, we constructed a PPI network encoded by 90 DEGs. We employed the CytoHubba plug-in to identify hub genes that were predominantly EGR1, FOS, FOSB, KLF2, and JUNB. The Fos gene family includes four members, including FOS, FOSB, FOSL1, and FOSL2, and regulates cellular proliferation, differentiation, and transformation [ 39 ]. JUNB is a protein-encoded gene located in the nucleoplasm that positively modulates RNA polymerase II transcription [ 40 ]. It forms heterodimers with FOS family proteins to create the AP-1 transcription complex, enhancing DNA-binding activity, transcriptional activity, and AP-1 consensus sequence specificity [ 41 , 42 ]. The AP-1 complex has been identified to facilitate the pathogenesis of osteoarthritis by binding to the promoter of inflammatory cytokines [ 43 ]. KLF2 encodes a zinc finger protein within the Kruppel family that is expressed during early mammalian development and functions in multiple growth and disease-related processes, such as adipogenesis, embryonic erythroid cell generation, epithelial cell integrity, inflammation, and T cell viability [ 44 ]. KLF2 is involved in osteoarthritis progression by regulating matrix metalloproteinases (MMPs), as reported by Takashi Aki et al. [ 45 ]. Finally, we explored the biological functions of EGR1 in the GTEx database. GSEA analysis revealed significant activation of inflammatory-related pathways, including EMT, TNF-α signaling via NF-κB, and the inflammatory response. Notably, the GSEA analysis of the osteoarthritis and muscle atrophy datasets included these three pathways. Therefore, EGR1 affects the process of muscle atrophy in osteoarthritis as it participates in these three pathways. Through experimentation, the involvement of the EGR1 gene in osteoarthritis and muscle atrophy has been effectively validated. Firstly, the direct regulatory influence of the TF EGR1 on the FOS, FOSB, KLF2, and JUNB genes was confirmed using real-time fluorescence quantitative polymerase chain reaction. Secondly, the vital role of the EGR1 gene in inflammation regulation was substantiated through an enzyme-linked immunosorbent assay. This regulatory mechanism has been observed to impact various inflammatory pathways, such as EMT, TNF-α signaling via NF-κB, and the inflammatory response, significantly altering the expression of proteins associated with inflammation. Lastly, the regulatory significance of the EGR1 gene in skeletal muscle apoptosis was affirmed through a flow cytometry assay. These experimental findings are consistent with the outcomes of bioinformatics analysis. In this study, we employed bioinformatics analysis to determine EGR1 as a key gene implicated in the muscle atrophy observed in patients with KOA. Nevertheless, there are still some limitations in the study design. Firstly, the possibility of bias should be acknowledged because of the small sample size utilized in the bioinformatics analysis. Secondly, further validation through in vivo and in vitro experiments is required. Nonetheless, current technology and capabilities only allow for the exploration of gene overexpression and knockdown via animal experiments, which may introduce some degree of error in comparison to studies involving humans. Additionally, confirmation of the results through collection of clinical samples is essential. Finally, a comprehensive understanding of the main targets is necessary to design effective treatment strategies for post-arthritic muscle atrophy resulting from knee osteoarthritis. 5 Conclusion The high expression of the transcription factor EGR1 has been established as a direct regulator of FOS, FOSB, KLF2, and JUNB. This regulatory mechanism has been found to affect various inflammatory pathways, such as EMT, TNF-α signaling via NF-κB, and inflammatory response. These pathways, in turn, have an impact on the postoperative muscle atrophy process in patients with KOA. Abbreviations DEG: differentially expressed gene; EGR1: early growth response gene-1; ELISA: enzyme-linked immunosorbent assay; EMT: epithelial mesenchymal transition; FC: fold change; FDR: false discovery rate; GEO: Gene Expression Omnibus database; GO: gene ontology; GO: BP: gene ontology biological process; GSEA: gene set enrichment analysis; IL-1β: Interleukin-1β; KOA: knee Osteoarthritis; MCC: Maximum Clique Centrality; NES: normalization enrichment score; PI: propidium iodide; PPI: protein-protein interaction network; qRT-PCR: reverse transcriptase real-time quantitative polymerase chain reaction; SD: Standard deviation; TF: transcription factor; TNF-α: tumor necrosis factor α; TKA: total knee arthroplasty; Declarations Data Availability All the data used and analyzed during the current study are available from the corresponding author upon reasonable request. Funding This research was funded by 1·3·5 project for disciplines of excellence, West China Hospital, Sichuan University (grant No: 2023HXFH012, ZYGD23033). Funds were used to cover experimental reagents and consumables. Conflict of interest All authors state that they have no competing interests. Acknowledgments Not applicable. References Silva JMS, Alabarse PVG, Teixeira VON, Freitas EC, de Oliveira FH, Chakr R, Xavier RM: Muscle wasting in osteoarthritis model induced by anterior cruciate ligament transection . PloS one 2018, 13 (4):e0196682. Franz A, Ji S, Bittersohl B, Zilkens C, Behringer M: Impact of a Six-Week Prehabilitation With Blood-Flow Restriction Training on Pre- and Postoperative Skeletal Muscle Mass and Strength in Patients Receiving Primary Total Knee Arthroplasty . Frontiers in physiology 2022, 13 :881484. Ethgen O, Bruyère O, Richy F, Dardennes C, Reginster JY: Health-related quality of life in total hip and total knee arthroplasty. 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Reardon K, Galea M, Dennett X, Choong P, Byrne E: Quadriceps muscle wasting persists 5 months after total hip arthroplasty for osteoarthritis of the hip: a pilot study . Internal medicine journal 2001, 31 (1):7-14. Amaro A, Amado F, Duarte JA, Appell HJ: Gluteus medius muscle atrophy is related to contralateral and ipsilateral hip joint osteoarthritis . International journal of sports medicine 2007, 28 (12):1035-1039. O'Reilly SC, Jones A, Muir KR, Doherty M: Quadriceps weakness in knee osteoarthritis: the effect on pain and disability . Annals of the rheumatic diseases 1998, 57 (10):588-594. McAlindon TE, Cooper C, Kirwan JR, Dieppe PA: Determinants of disability in osteoarthritis of the knee . Annals of the rheumatic diseases 1993, 52 (4):258-262. Thomas AC, Stevens-Lapsley JE: Importance of attenuating quadriceps activation deficits after total knee arthroplasty . Exercise and sport sciences reviews 2012, 40 (2):95-101. Barber-Westin S, Noyes FR: Blood Flow-Restricted Training for Lower Extremity Muscle Weakness due to Knee Pathology: A Systematic Review . Sports health 2019, 11 (1):69-83. Hughes L, Rosenblatt B, Gissane C, Paton B, Patterson SD: Interface pressure, perceptual, and mean arterial pressure responses to different blood flow restriction systems . Scandinavian journal of medicine & science in sports 2018, 28 (7):1757-1765. Loenneke JP, Wilson JM, Marín PJ, Zourdos MC, Bemben MG: Low intensity blood flow restriction training: a meta-analysis . European journal of applied physiology 2012, 112 (5):1849-1859. Hurley MV: The role of muscle weakness in the pathogenesis of osteoarthritis . Rheumatic diseases clinics of North America 1999, 25 (2):283-298, vi. Zhang W, Tong H, Zhang Z, Shao S, Liu D, Li S, Yan Y: Transcription factor EGR1 promotes differentiation of bovine skeletal muscle satellite cells by regulating MyoG gene expression . Journal of cellular physiology 2018, 233 (1):350-362. Tsai-Morris CH, Cao XM, Sukhatme VP: 5' flanking sequence and genomic structure of Egr-1, a murine mitogen inducible zinc finger encoding gene . Nucleic acids research 1988, 16 (18):8835-8846. Cao XM, Koski RA, Gashler A, McKiernan M, Morris CF, Gaffney R, Hay RV, Sukhatme VP: Identification and characterization of the Egr-1 gene product, a DNA-binding zinc finger protein induced by differentiation and growth signals . Molecular and cellular biology 1990, 10 (5):1931-1939. Yu J, Zhang SS, Saito K, Williams S, Arimura Y, Ma Y, Ke Y, Baron V, Mercola D, Feng GS et al : PTEN regulation by Akt-EGR1-ARF-PTEN axis . The EMBO journal 2009, 28 (1):21-33. Havis E, Duprez D: EGR1 Transcription Factor is a Multifaceted Regulator of Matrix Production in Tendons and Other Connective Tissues . International journal of molecular sciences 2020, 21 (5). Orgeur M, Martens M, Leonte G, Nassari S, Bonnin MA, Börno ST, Timmermann B, Hecht J, Duprez D, Stricker S: Genome-wide strategies identify downstream target genes of chick connective tissue-associated transcription factors . Development (Cambridge, England) 2018, 145 (7). Lejard V, Blais F, Guerquin MJ, Bonnet A, Bonnin MA, Havis E, Malbouyres M, Bidaud CB, Maro G, Gilardi-Hebenstreit P et al : EGR1 and EGR2 involvement in vertebrate tendon differentiation . The Journal of biological chemistry 2011, 286 (7):5855-5867. McMahon AP, Champion JE, McMahon JA, Sukhatme VP: Developmental expression of the putative transcription factor Egr-1 suggests that Egr-1 and c-fos are coregulated in some tissues . Development (Cambridge, England) 1990, 108 (2):281-287. Sun X, Huang H, Pan X, Li S, Xie Z, Ma Y, Hu B, Wang J, Chen Z, Shi P: EGR1 promotes the cartilage degeneration and hypertrophy by activating the Krüppel-like factor 5 and β-catenin signaling . Biochimica et biophysica acta Molecular basis of disease 2019, 1865 (9):2490-2503. Chen Z, Yue SX, Zhou G, Greenfield EM, Murakami S: ERK1 and ERK2 regulate chondrocyte terminal differentiation during endochondral bone formation . Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research 2015, 30 (5):765-774. Grimbacher B, Aicher WK, Peter HH, Eibel H: Measurement of transcription factor c-fos and EGR-1 mRNA transcription levels in synovial tissue by quantitative RT-PCR . Rheumatology international 1997, 17 (3):109-112. Aicher WK, Heer AH, Trabandt A, Bridges SL, Jr., Schroeder HW, Jr., Stransky G, Gay RE, Eibel H, Peter HH, Siebenlist U et al : Overexpression of zinc-finger transcription factor Z-225/Egr-1 in synoviocytes from rheumatoid arthritis patients . Journal of immunology (Baltimore, Md : 1950) 1994, 152 (12):5940-5948. Nebbaki SS, El Mansouri FE, Afif H, Kapoor M, Benderdour M, Duval N, Pelletier JP, Martel-Pelletier J, Fahmi H: Egr-1 contributes to IL-1-mediated down-regulation of peroxisome proliferator-activated receptor γ expression in human osteoarthritic chondrocytes . Arthritis research & therapy 2012, 14 (2):R69. Alexander D, Judex M, Meyringer R, Weis-Klemm M, Gay S, Müller-Ladner U, Aicher WK: Transcription factor Egr-1 activates collagen expression in immortalized fibroblasts or fibrosarcoma cells . Biological chemistry 2002, 383 (12):1845-1853. Bossis G, Malnou CE, Farras R, Andermarcher E, Hipskind R, Rodriguez M, Schmidt D, Muller S, Jariel-Encontre I, Piechaczyk M: Down-regulation of c-Fos/c-Jun AP-1 dimer activity by sumoylation . Molecular and cellular biology 2005, 25 (16):6964-6979. Lin Z, Miao J, Zhang T, He M, Wang Z, Feng X, Bai L: JUNB-FBXO21-ERK axis promotes cartilage degeneration in osteoarthritis by inhibiting autophagy . Aging cell 2021, 20 (2):e13306. Huber R, Kunisch E, Glück B, Egerer R, Sickinger S, Kinne RW: [Comparison of conventional and real-time RT-PCR for the quantitation of jun protooncogene mRNA and analysis of junB mRNA expression in synovial membranes and isolated synovial fibroblasts from rheumatoid arthritis patients] . Zeitschrift fur Rheumatologie 2003, 62 (4):378-389. Benderdour M, Tardif G, Pelletier JP, Di Battista JA, Reboul P, Ranger P, Martel-Pelletier J: Interleukin 17 (IL-17) induces collagenase-3 production in human osteoarthritic chondrocytes via AP-1 dependent activation: differential activation of AP-1 members by IL-17 and IL-1beta . The Journal of rheumatology 2002, 29 (6):1262-1272. Huber R, Augsten S, Kirsten H, Zell R, Stelzner A, Thude H, Eidner T, Stuhlmüller B, Ahnert P, Kinne RW: Identification of New, Functionally Relevant Mutations in the Coding Regions of the Human Fos and Jun Proto-Oncogenes in Rheumatoid Arthritis Synovial Tissue . Life (Basel, Switzerland) 2020, 11 (1). Gao X, Jiang S, Du Z, Ke A, Liang Q, Li X: KLF2 Protects against Osteoarthritis by Repressing Oxidative Response through Activation of Nrf2/ARE Signaling In Vitro and In Vivo . Oxidative medicine and cellular longevity 2019, 2019 :8564681. Aki T, Hashimoto K, Ogasawara M, Itoi E: A whole-genome transcriptome analysis of articular chondrocytes in secondary osteoarthritis of the hip . PloS one 2018, 13 (6):e0199734. Additional Declarations No competing interests reported. Supplementary Files Additionalfile1.xlsx Additionalfile2.xlsx Additionalfile3.xlsx Additionalfile4.xlsx Cite Share Download PDF Status: Published Journal Publication published 01 Oct, 2024 Read the published version in Journal of Orthopaedic Surgery and Research → Version 1 posted Editorial decision: Revision requested 03 Sep, 2024 Reviews received at journal 31 Aug, 2024 Reviews received at journal 31 Aug, 2024 Reviewers agreed at journal 10 Aug, 2024 Reviewers agreed at journal 10 Aug, 2024 Reviewers agreed at journal 09 Aug, 2024 Reviewers agreed at journal 09 Aug, 2024 Reviewers invited by journal 07 Aug, 2024 Editor assigned by journal 06 Aug, 2024 Submission checks completed at journal 06 Aug, 2024 First submitted to journal 01 Aug, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4839822","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":346635467,"identity":"0a3496b8-69a2-4ef5-ae8f-3f2bd1343481","order_by":0,"name":"Xiao-yang Liu","email":"","orcid":"","institution":"Department of Orthopedics, Orthopaedic Research Institute, West China Hospital, Sichuan University, #37 Guoxue Road, Chengdu, 610041, People’s Republic of China.","correspondingAuthor":false,"prefix":"","firstName":"Xiao-yang","middleName":"","lastName":"Liu","suffix":""},{"id":346635468,"identity":"8c3029ca-fbc4-48c0-ab08-5e85107ee105","order_by":1,"name":"Qiu-ping Yu","email":"","orcid":"","institution":"Health Management Center, West China Hospital/West China School of Medicine, Sichuan University, Chengdu 610041, China.","correspondingAuthor":false,"prefix":"","firstName":"Qiu-ping","middleName":"","lastName":"Yu","suffix":""},{"id":346635469,"identity":"b3cb417c-ebdb-4194-9965-09f7d60ece3c","order_by":2,"name":"Si-qin Guo","email":"","orcid":"","institution":"Department of Orthopedics, Orthopaedic Research Institute, West China Hospital, Sichuan University, #37 Guoxue Road, Chengdu, 610041, People’s Republic of China.","correspondingAuthor":false,"prefix":"","firstName":"Si-qin","middleName":"","lastName":"Guo","suffix":""},{"id":346635471,"identity":"b7b9d24f-22ba-456f-a3ce-cb18f1dd8d8e","order_by":3,"name":"Xu-ming Chen","email":"","orcid":"","institution":"Department of Orthopedics, Orthopaedic Research Institute, West China Hospital, Sichuan University, #37 Guoxue Road, Chengdu, 610041, People’s Republic of China.","correspondingAuthor":false,"prefix":"","firstName":"Xu-ming","middleName":"","lastName":"Chen","suffix":""},{"id":346635474,"identity":"169dfb26-0a8a-4df2-ae0a-3527a8f1205a","order_by":4,"name":"Wei-Nan Zeng","email":"","orcid":"","institution":"Department of Orthopedics, Orthopaedic Research Institute, West China Hospital, Sichuan University, #37 Guoxue Road, Chengdu, 610041, People’s Republic of China.","correspondingAuthor":false,"prefix":"","firstName":"Wei-Nan","middleName":"","lastName":"Zeng","suffix":""},{"id":346635478,"identity":"19e6fdb7-6611-4546-9dbe-ba7beaf187f8","order_by":5,"name":"Zong-ke Zhou","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5ElEQVRIiWNgGAWjYDACCTjNfADCOkC8FrYEkrXwGBCnRX528zHJHxV37Ga293z+8LONQY7vRgLj5wI8WhjnHEuTkDjzLHk2z9ltkr1tDMaSNxKYpWfg0cIskWMmYdh2OFlOIncbM2MbQ+KGGwlszDx4tLCBtCSCtMi/efwZqKWeoBYekJaDbYftpCV4GKSBWhIMCGmRkEhLtmw4czhBsifNTLLnnIThzDMPm6XxaZGfkXzw5o+Kw/YSxw8//vCjzEae73jywc/4tMBAYgPUViBmbCBCAwODPVGqRsEoGAWjYGQCAMdVSLG5Q8fDAAAAAElFTkSuQmCC","orcid":"","institution":"Department of Orthopedics, Orthopaedic Research Institute, West China Hospital, Sichuan University, #37 Guoxue Road, Chengdu, 610041, People’s Republic of China.","correspondingAuthor":true,"prefix":"","firstName":"Zong-ke","middleName":"","lastName":"Zhou","suffix":""}],"badges":[],"createdAt":"2024-08-01 06:44:04","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4839822/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4839822/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s13018-024-05109-9","type":"published","date":"2024-10-01T15:57:18+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":64384269,"identity":"846926a5-3cb3-404b-a894-b9c7142fcdb0","added_by":"auto","created_at":"2024-09-12 12:18:31","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":5713,"visible":true,"origin":"","legend":"\u003cp\u003eThe flowchart of the research process. GEO: Gene Expression Omnibus database; GSEA: Gene set enrichment analysis; DEG: differentially expressed gene; TF: Transcription Factor; EGR1: early growth response gene-1; GO: BP: gene ontology biological process; PPI: protein-protein interaction.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4839822/v1/373e9b54c8bc42d663273979.png"},{"id":64384271,"identity":"d1efcdc8-2050-4f09-8073-52db5e14e465","added_by":"auto","created_at":"2024-09-12 12:18:31","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":404114,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(A)\u003c/strong\u003e Volcano plots, functional enrichment analysis, and gene set enrichment analysis of the GSE82107 dataset and the GSE205431 dataset; \u003cstrong\u003e(B)\u003c/strong\u003e Venn diagram and heatmap of 138 common differentially expressed genes (DEGs); \u003cstrong\u003e(C)\u003c/strong\u003e Venn diagram of 16 transcription factors (TFs) and differential expression of the target TF early growth response gene-1 (EGR1) in GSE82107.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4839822/v1/1e165504ab503154fcb532e2.png"},{"id":64386252,"identity":"2bcf1639-30e3-45aa-a4cd-573ed61b61da","added_by":"auto","created_at":"2024-09-12 12:34:31","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":3384797,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(A)\u003c/strong\u003e Functional enrichment analysis of gene ontology biological process in 90 common differentially expressed genes (DEGs) regulated by early growth response gene-1 (EGR1). \u003cstrong\u003e(B)\u003c/strong\u003e Overview of the protein-protein interaction network of 90 common DEGs through Cytoscape. The larger size of the points, the higher degree of the genes;\u003cstrong\u003e(C)\u003c/strong\u003e Top 5 hub genes interaction networks; the darker the color, the more powerful the critical degree; the darker the color, the more powerful the critical degree.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4839822/v1/4b623d200c43f71d74f7374c.png"},{"id":64385458,"identity":"a8b77631-56e0-4c58-9e87-dbb26378606e","added_by":"auto","created_at":"2024-09-12 12:26:31","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":383224,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional mining of early growth response gene-1 (EGR1). \u003cstrong\u003e(A)\u003c/strong\u003e Functional enrichment analysis of EGR1 in the GTEx database; \u003cstrong\u003e(B)\u003c/strong\u003e Biological pathway of EGR1 in the Hallmark gene set.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4839822/v1/ef9ebecaacc7e94e5929cb4b.png"},{"id":64384276,"identity":"d3ba8681-95f1-40b8-986d-b66ab35a60e4","added_by":"auto","created_at":"2024-09-12 12:18:32","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":475039,"visible":true,"origin":"","legend":"\u003cp\u003eVerification of the early growth response gene-1 (EGR1) gene was conducted using qRT-PCR. \u003cstrong\u003e(A)\u003c/strong\u003e The expression levels of relevant genes were assessed under normal expression of the EGR1 gene in the control, arthritis, and muscle atrophy groups. \u003cstrong\u003e(B)\u003c/strong\u003e The expression levels of relevant genes were analyzed under overexpression of the EGR1 gene in the control, arthritis, and muscle atrophy groups. \u003cstrong\u003e(C)\u003c/strong\u003e The expression levels of relevant genes were examined under knockdown of the EGR1 gene in the control, arthritis, and muscle atrophy groups. The significance levels were set at *p \u0026lt; 0.05, **p \u0026lt; 0.01, and ***p \u0026lt; 0.001. The error bars were used to represent the standard deviation. MA: muscle atrophy.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-4839822/v1/17f66ce3f77fb2f4c15995dc.png"},{"id":64385459,"identity":"6e76acac-64d9-4322-acf5-08aaf874d1a8","added_by":"auto","created_at":"2024-09-12 12:26:31","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":79748,"visible":true,"origin":"","legend":"\u003cp\u003eELISA analysis was conducted to confirm the expression levels of IL-1β and TNF-α in synovial fluid. \u003cstrong\u003e(A)\u003c/strong\u003e The expression of IL-1β was examined under normal expression, overexpression, and knockdown of the early growth response gene-1 (EGR1) gene. \u003cstrong\u003e(B)\u003c/strong\u003e The expression of TNF-α was evaluated under normal expression, overexpression, and knockdown of the EGR1 gene. The significance levels were set at *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, and ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001. The error bars were used to represent the standard deviation.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-4839822/v1/2f005e0106d288dec253833b.png"},{"id":64384278,"identity":"ad14a30f-9810-4b80-a927-f1f32f58e97c","added_by":"auto","created_at":"2024-09-12 12:18:32","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":808519,"visible":true,"origin":"","legend":"\u003cp\u003eFlow cytometry was conducted to evaluate the apoptotic status of skeletal muscle cells. \u003cstrong\u003e(A)\u003c/strong\u003e Apoptosis profiles of skeletal muscle cells were analyzed under conditions of normal expression, overexpression, and knockdown of the early growth response gene-1 (EGR1) gene. \u003cstrong\u003e(B)\u003c/strong\u003e Apoptosis statistics of skeletal muscle cells were assessed under conditions of normal expression, overexpression, and knockdown of the EGR1 gene.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-4839822/v1/245b9bc0a154eba0c2273a96.png"},{"id":66096781,"identity":"38a29e32-0fb8-4cfd-88db-a3748936f2f3","added_by":"auto","created_at":"2024-10-07 16:10:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7100510,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4839822/v1/309c2b89-33e0-44f1-9a87-df46bb13b146.pdf"},{"id":64386254,"identity":"76bf910f-2cef-464c-9e00-6294381a8395","added_by":"auto","created_at":"2024-09-12 12:34:31","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":112664,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4839822/v1/0636c6d1fb03cabc15d2c8e7.xlsx"},{"id":64387954,"identity":"eb95bbd8-b739-4215-8669-ab2e99516b8a","added_by":"auto","created_at":"2024-09-12 12:42:31","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":15199,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4839822/v1/57e164db7fb574b837420c94.xlsx"},{"id":64384280,"identity":"3ecea033-2a3b-4413-8db9-1965d5704ad4","added_by":"auto","created_at":"2024-09-12 12:18:32","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":137755,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4839822/v1/dad7b4a74cbdf24d3ce2bd3d.xlsx"},{"id":64384277,"identity":"74f070a4-3161-43b9-8c8f-dd3163cd34b0","added_by":"auto","created_at":"2024-09-12 12:18:32","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":13411,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile4.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4839822/v1/2d9fe532f5efe366e6872df5.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"High expression of transcription factor EGR1 is associated with postoperative muscle atrophy in patients with knee osteoarthritis undergoing total knee arthroplasty","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eMuscle atrophy, characterized by the reduction in muscle mass and strength, frequently occurs as a complication after total knee arthroplasty (TKA) in patients with knee osteoarthritis (KOA) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Although TKA has demonstrated significant improvements in pain, active function, and quality of life in patients [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], research has demonstrated a high incidence of postoperative muscle atrophy that can persist for many years, particularly presenting as quadricep weakness [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The quadriceps muscle plays a critical role in knee extension and overall lower extremity function, yet it is particularly vulnerable to atrophy following surgery. This weakness may lead to abnormal gait, lower physical capacity, and an increased likelihood of falls and other postoperative complications [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. With more than 1.1\u0026nbsp;million TKA procedures performed in the United States alone in 2009 [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], muscle atrophy-induced mobility difficulties have emerged as a significant public health concern.\u003c/p\u003e \u003cp\u003eThe relationship between KOA and muscle atrophy is multifaceted and bidirectional. While muscle weakness and atrophy may catalyze KOA development [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], the presence of KOA can negatively impact muscle function and further exacerbate muscle atrophy [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Advanced imaging techniques have exhibited that the cross-sectional area of muscles in the affected limb of KOA patients is reduced by 19% compared to their normal limb [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Several clinical studies suggest that muscle atrophy is a result of a combination of surgical trauma, immobilization, disuse, and altered neuromuscular function [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Nevertheless, the underlying biological mechanisms concerning muscle atrophy in post-TKA patients are yet to be definitively determined.\u003c/p\u003e \u003cp\u003eThe primary objective of this study was to investigate the potential cellular and molecular mechanisms underlying muscle atrophy in patients with KOA following TKA. We adopted a bioinformatics approach and analyzed two datasets from the Gene Expression Omnibus (GEO) database, including the muscle atrophy and osteoarthritis datasets. Through comparative analysis, we identified common differentially expressed genes (DEGs) shared by both datasets and subjected them to transcription factor enrichment analysis. The enriched transcription factors (TFs) were intersected with the common DEGs to obtain the target TF most significantly expressed in both osteoarthritis and muscle atrophy. Subsequently, we leveraged protein-protein interaction (PPI) network analysis to identify potential hub genes and used gene set enrichment analysis (GSEA) to examine both DEGs and target TF for pathways associated with muscle atrophy. Ultimately, we validated the expression and function of the target TF leveraging reverse transcriptase real-time quantitative polymerase chain reaction (qRT-PCR), enzyme-linked immunosorbent assay (ELISA), and Flow Cytometry. This investigation adds to our understanding of the mechanism and diagnosis of muscle atrophy in post-TKA patients.\u003c/p\u003e"},{"header":"2 Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data Collection\u003c/h2\u003e \u003cp\u003eTwo microarray datasets, GSE82107 and GSE205431, were downloaded from the GEO database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ncbi.nlm.nih.gov/geo\u003c/span\u003e\u003cspan address=\"http://www.ncbi.nlm.nih.gov/geo\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) using \"osteoarthritis\" and \"muscle atrophy\" as keywords. The GSE82107 dataset, utilized in the GPL570 platform, accommodated synovial tissue samples from ten patients diagnosed with osteoarthritis and seven healthy controls. The GSE205431 dataset, employed in the GPL24676 platform, encapsulated RNA-sequencing data of skeletal muscle tissue samples from a total of twenty subjects suffering from end-stage osteoarthritis, including non-surgical limb samples (musculus vastus lateralis, n\u0026thinsp;=\u0026thinsp;20) and surgical limb samples (vastus medialis, tensor fasciae latae, or gluteus maximus, n\u0026thinsp;=\u0026thinsp;20). Moreover, count and TPM expression matrices of RNA sequencing data from 803 healthy human skeletal muscle tissue samples were downloaded from the GTEx database.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Analysis of DEGs\u003c/h2\u003e \u003cp\u003eThe differential expression analysis was carried out using empirical Bayesian linear models within the \"Limma\" package of R software to detect DEGs in the osteoarthritis dataset (GSE82107). For the RNA-sequencing muscle atrophy dataset (GSE205431), the DESeq2 package, designed for data with a Poisson distribution, was utilized for differential expression analysis. The statistical significance threshold for DEGs was determined by adjusting the \u003cem\u003eP\u003c/em\u003e-value (false discovery rate [FDR]\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and setting the expression to an absolute log2 fold change (FC)\u0026thinsp;\u0026gt;\u0026thinsp;0.5. Volcano plots were used as a visual representation for all the DEGs. The DEGs common in both datasets were identified by a Venn diagram tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://bioinfogp.cnb.csic.es/tools/venny/\u003c/span\u003e\u003cspan address=\"https://bioinfogp.cnb.csic.es/tools/venny/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and presented through heatmaps.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 GSEA for the Two Microarray Datasets\u003c/h2\u003e \u003cp\u003eGSEA ranks the differential expression levels between different sample groups using pre-defined gene sets to determine if the gene sets are enriched at the top or bottom of the ranked list. The Hallmark gene set was used in this research for enrichment analysis. The ClusterProfiler (3.14) R package was used to analyze GSEA on the GSE82107 and GSE205431 datasets to determine significant functional and pathway differences based on differential expressional analysis results. The normalization enrichment score (NES) and FDR were calculated by setting random numbers to 1000. A gene set was considered to be significantly enriched when fulfilling the conditions NES\u0026thinsp;\u0026ge;\u0026thinsp;1.0, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05, and FDR\u0026thinsp;\u0026le;\u0026thinsp;0.25.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Analysis of TF Enrichment and Identification of Target TF\u003c/h2\u003e \u003cp\u003eCheA3 web (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://maayanlab.cloud/chea3/\u003c/span\u003e\u003cspan address=\"https://maayanlab.cloud/chea3/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) is a computational platform that enables the user to investigate the regulatory relationships between genes and transcription factors by entering a list of genes or selecting from predefined gene sets. It uses various algorithms and parameters to identify enriched transcription factors, gene ontology terms, and pathways. To enrich the corresponding transcription factors, the ChEA3 website was used to upload the common DEGs from the osteoarthritis and muscle atrophy datasets. The Venn diagram tool was utilized to obtain the intersection of enriched transcription factors with common DEGs to identify the TFs that exhibited significant differential expression across both datasets. Finally, the TF that could potentially regulate the most common DEGs was selected as the target TF.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Functional Annotation of Genes Regulated by Target TF\u003c/h2\u003e \u003cp\u003eThe Clue gene ontology (GO) method, which combines statistical tests with visual representations, is an effective approach used in biological research to interpret large datasets and reveal functional relationships between genes underlying complex diseases. This method is frequently used in biological research to uncover functional relationships between genes and to gain insights into underlying biological processes in complex diseases. To explain the potential biological mechanisms of these genes, the common DEGs regulated by the target TF were extracted, and gene ontology biological process (GO: BP) functional enrichment analysis was carried out using the Clue GO plug-in in Cytoscape software.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 PPI network construction and hub target TF-regulated genes identification\u003c/h2\u003e \u003cp\u003eA PPI refers to the process by which proteins form noncovalent bonds to create a protein complex. The construction of a PPI network facilitates the understanding of the molecular mechanisms of biological processes. The STRING database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.string-db.org/\u003c/span\u003e\u003cspan address=\"http://www.string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) is an online resource used for providing comprehensive information concerning PPI and functional associations among various species. Common DEGs that potentially regulate TFs were incorporated into the STRING database to scrutinize their interactions and produce a PPI network. Cytoscape (Version 3.7.1) is an open-source tool used for analyzing and visualizing molecular interaction networks, where the outcomes of the STRING database were imported. CytoHubba plug-in was applied to identify the top five hub genes according to their weight coefficients by using the Maximum Clique Centrality (MCC) algorithm. Finally, Cytoscape visualizes the five hub genes and their closely associated common DEGs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Exploring the biological characteristics of target TF\u003c/h2\u003e \u003cp\u003eTo investigate the biological function of the target TF, we selected 803 RNA sequencing data samples from the GTEx database that were derived from skeletal muscle tissue. We used the DESeq2 package to perform differential expression analysis with the median expression level of the target TF used as the grouping condition. Next, we performed the HALLMARK gene set enrichment analysis using the ClusterProfile package with screening conditions of \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and FDR\u0026thinsp;\u0026le;\u0026thinsp;0.25.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Experimental animals and grouping\u003c/h2\u003e \u003cp\u003e This experimental study received approval from the Animal Ethics Committee of Sichuan University. SD rats were primarily chosen for constructing the osteoarthritis model and conducting subsequent research. The study comprised three groups categorized by distinct expression levels of the early growth response gene-1 (EGR1): the normal group, the EGR1 overexpression group, and the EGR1 knockdown group. Rats in the normal group were obtained from the Experimental Animal Center of Sichuan University, while those in the EGR1 overexpression and knockdown groups were sourced from Cyagen Biosciences (Guangzhou) Inc. The experimental animals were individually housed in standard cages at the SPF Experimental Animal Center of Sichuan University (three rats per cage). They had ad libitum access to food and water under controlled temperature and humidity conditions and underwent a one-week acclimatization period within a 12-hour light and 12-hour dark cycle before the experiment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.9 Construction of the osteoarthritis model\u003c/h2\u003e \u003cp\u003eFollowing rat anesthesia, the skin on the medial side of the knee joint was aseptically cleansed, and a longitudinal incision approximately three centimeters in length was performed. The infrapatellar fold, connecting to the intercondylar fossa, was incised to reveal the underlying anterior cruciate ligament. Using microscissors, the anterior cruciate ligament was transected near the femur. The successful detachment of the anterior cruciate ligament was confirmed through an anterior drawer test.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.10 Experimental cells\u003c/h2\u003e \u003cp\u003eThe experimental cells were procured from Yagi Biotechnology (Shanghai, China) Inc, and genetic manipulation of the EGR1 gene was accomplished with lentiviral constructs. The cells were cultured in DMEM medium supplemented with 10% fetal bovine serum and 1% antibiotics (penicillin/streptomycin). They were maintained in a cell culture incubator under constant conditions of temperature (37\u0026deg;C) and humidity, with a 5% CO2 atmosphere.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.11 qRT-PCR\u003c/h2\u003e \u003cp\u003eTo validate the expression of target transcription factors, the extraction of total RNA from chondrocytes was done using the TRIzol reagent (Takara, Japan). The cDNA synthesis was performed with the use of the PrimeScript RT kit (Takara, Japan). The qRT-PCR was executed by following the instructions provided using the SYBR Green method (ES Science, China, QP002). The specific primer sequences used for the qRT-PCR with cDNA are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The calculation of mRNA levels for the targeted gene involved the 2-ΔΔCt method, which was then normalized to GAPDH.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe primer sequence for RT-qPCR amplification.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForward\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReverse\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEGR1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCCGAGCGAACAACCCTATGA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAGGCTGAAAAGGGGTTCAGG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFOS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGCCTTCACCCTGCCTCTTC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGCTCCATGTTGCTAATGTTCTTGA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFOSB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTGGGCCTTCAACTAGCACAAG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGCTCCCTCCGACGGTTTCTG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJUNB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTACCTCCCACATGCACCACC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCGCTTTCGCTCCACTTTGAT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKLF2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGCGCTTTCGGTCTCTTCGAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGCAGTTGGTGTAGCTGCAAG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGAPDH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGACATCAAGAAGGTAATGAAGC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGAAGGTGGAAGAGTGGGAGTT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e2.12 ELISA\u003c/h2\u003e \u003cp\u003eThe concentrations of tumor necrosis factor-α (TNF-α) and interleukin-1β (IL-1β) were detected following the instructions provided with the ELISA kit. Briefly, the reaction pore was sequentially filled with the standard, sample, antibody, and HRP-streptavidin, and the mixture was incubated at 37 ℃. Subsequently, the chromogenic solution and stop solution were added to the reaction pore in a sequential manner. The OD value was then measured using an enzyme labeling instrument, and the protein concentration was calculated based on the standard curve.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e2.13 Flow Cytometry\u003c/h2\u003e \u003cp\u003eThe apoptosis rate was determined using flow cytometry. Following cell collection, flow cytometry analysis was conducted using the Annexin V/FITC apoptosis detection kit I (BD Biosciences, Franklin Lake, NJ, USA) in accordance with the manufacturer's instructions. Annexin V single-positive cells were indicative of early apoptosis, propidium iodide (PI) single-positive cells were indicative of necrosis, and PI and Annexin V double-positive cells indicated late apoptosis. In order to assess skeletal muscle cell apoptosis in the context of osteoarthritis, skeletal muscle cells were incubated with 20 \u0026micro; PE-labeled EGR1 (BD Bioscience) in the dark at room temperature. Subsequent analysis was performed using MODFitLT5.0 software (BD Biosciences).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e2.14 Statistical Analysis\u003c/h2\u003e \u003cp\u003eAll data were presented as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) assessed by the Shapiro-Wilk test for data normality. The Mann-Whitney \u003cem\u003eU\u003c/em\u003e test was employed to compare two groups, while one-way analysis of variance (ANOVA) followed by the Bonferroni post-test was used for comparisons among multiple groups of samples. GraphPad Prism 9 software was utilized to plot and analyze the data. Statistical significance was denoted as * for \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, ** for \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, and *** for \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Screening of DEGs\u003c/h2\u003e \u003cp\u003eScreening for DEGs was performed according to the study design flowchart presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Using a cutoff criterion of an adjusted p-value of less than 0.05 and |logFC| \u0026gt; 0.5, a total of 1582 DEGs were identified from the GSE82107 dataset, of which 700 were upregulated and 882 were downregulated in the osteoarthritis group \u003cb\u003e(Additional file 1)\u003c/b\u003e. In the GSE205431 dataset, 1229 DEGs were identified, of which 1169 were upregulated and 60 were downregulated \u003cb\u003e(Additional file 1)\u003c/b\u003e. To validate the results, volcano plots \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e were drawn. The DEGs from the two datasets were intersected by a Venn diagram, resulting in 138 common DEGs \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB \u003cb\u003eand Additional file 2)\u003c/b\u003e. Finally, the corresponding heat map is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e3.2 GSEA of two microarray datasets\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eThe GSEA was performed on the microarray datasets (GSE82107 and GSE205431) to identify the gene sets that were significantly differentially expressed between the osteoarthritis and muscle atrophy groups compared to the control group. The Hallmark gene set was used as the predefined gene set. In the GSE82107 dataset, the gene sets involved in epithelial mesenchymal transition (EMT), TNF-α signaling via NF-κB, and inflammatory response pathways were significantly activated in the osteoarthritis groups \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e. Similarly, in the GSE205431 dataset, the activation of EMT, TNF-α signaling via NF-κB, and inflammatory response pathways was also observed in the muscle atrophy group \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e. These results suggest similar biological processes in osteoarthritis and muscle atrophy. Furthermore, osteoarthritis may cause muscle atrophy through the activation of these three shared pathways.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.3 EGR1 is a target TF\u003c/h2\u003e \u003cp\u003eTFs play an essential role in regulating gene expression. To further explore the mechanism between osteoarthritis and muscle atrophy, we performed TF enrichment analysis on the 138 common DEGs. A total of 1632 TFs were enriched through the CheA3 transcription factor enrichment analysis website \u003cb\u003e(Additional file 3)\u003c/b\u003e. The enriched TFs were then intersected with the common DEGs using a Venn diagram intersection to obtain 16 TFs with significant differential expression in both the GSE82107 and GSE205431 datasets \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC \u003cb\u003eand\u003c/b\u003e Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. We finally selected EGR1 as the target TF due to the up to 90 common DEGs it regulates \u003cb\u003e(Additional file 4)\u003c/b\u003e. The differential expression of EGR1 was visualized using a box plot \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFeatures of 16 Transcription factors.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFull name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRegulation in GSE82107\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRegulation in GSE205431\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEGR1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEarly growth response 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUP\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFOS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFOS proto-oncogene\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUP\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFOSB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFOSB proto-oncogene\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUP\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJUNB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJUNB proto-oncogene\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUP\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKLF2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKLF transcription factor 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUP\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEGR2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEarly growth response 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUP\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSOX4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSRY-box transcription factor 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUP\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNR4A2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNuclear receptor subfamily 4 group A member 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUP\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNANOG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNanog homeobox\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDOWN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUP\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIRX5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIroquois homeobox 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUP\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMYCL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMYCL proto-oncogene\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDOWN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUP\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZNF580\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eZinc finger protein 580\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUP\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZNF428\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eZinc finger protein 428\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUP\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCIC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCapicua transcriptional repressor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUP\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTCF7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTranscription factor 7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDOWN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUP\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZBTB7B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eZinc finger and BTB domain containing 7B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDOWN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUP\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Functional annotation of EGR1 regulatory genes\u003c/h2\u003e \u003cp\u003eTo verify the functions of the 90 common DEGs that can be regulated by EGR1, biological function annotation analysis was performed using the Clue GO plug-in in the Cytoscape software. The GO: BP enrichment results showed that the 90 common DEGs were involved in biological processes related to muscle atrophy \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Construction of PPI network and hub genes for EGR1 regulatory genes\u003c/h2\u003e \u003cp\u003eThe 90 common DEGs from the previous analysis were used to screen for proteins interacting with them and to construct a PPI network using the STRING database \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e. The results were imported into the Cytoscape software, and node connectivity was calculated using the MCC algorithm in the CytoHubba plug-in. The five top hub genes were EGR1, FOS, FOSB, KLF2, and JUNB genes, which were colored red as nodes in the PPI network \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC\u003cb\u003e)\u003c/b\u003e. These genes have strong interactions, with darker colors indicating higher rank, pointing towards a possible pathophysiological mechanism linking osteoarthritis to muscle atrophy.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Biological characteristics of EGR1\u003c/h2\u003e \u003cp\u003eThe GSEA of 803 sequencing data from skeletal muscle tissues in the GTEx database indicated upregulation of EMT, TNF-α signaling via NF-κB, inflammatory response, Interferon-γ response, allograft rejection, and Kras signaling. In addition, high EGR1 expression was positively correlated with apoptosis, estrogen response late, coagulation, and apical junction pathways \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Validation of EGR1\u003c/h2\u003e \u003cp\u003eqRT-PCR was utilized to preliminary detect hub gene expression in osteoarthritis chondrocytes \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. The results indicated that the expression of EGR1 significantly increased in the osteoarthritis group and the muscle atrophy group compared to the healthy control group under normal EGR1 expression conditions, with a significant statistical difference (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). The expression levels of other genes related to EGR1, including Fos, FosB, KLF2, and JunB, were also significantly higher compared to the normal control group, with statistically significant differences (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). By manipulating EGR1 through overexpression and knockdown, we found that when the EGR1 gene was overexpressed, the other four genes' expression levels significantly increased \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e. Conversely, when the EGR1 gene was knocked down, the expression levels of the other four genes significantly decreased in response \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC\u003cb\u003e)\u003c/b\u003e. These findings align with the results of bioinformatics analysis, suggesting that EGR1 functions as a transcription factor that regulates the expression of the other four genes, thereby impacting muscle atrophy in osteoarthritis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eELISA was employed to determine IL-1β and TNF-α expression levels in the synovial fluid of osteoarthritis patients \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA-B\u003cb\u003e)\u003c/b\u003e. The results demonstrated that, under normal EGR1 expression conditions, the levels of IL-1β and TNF-α expression in the osteoarthritis group exhibited significant elevation compared to the control group. When the EGR1 gene was overexpressed or knocked down, the expression levels of IL-1β and TNF-α in osteoarthritis either increased or decreased significantly and these alterations were statistically significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). This finding demonstrates the significant regulatory role of the EGR1 gene in the inflammatory pathway.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAdditionally, the influence of EGR1 on apoptosis in skeletal muscle cells was extensively investigated through the implementation of flow cytometry analysis \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA and \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e. The obtained results revealed that manipulating the expression levels of the EGR1 gene in osteoarthritis chondrocytes led to a corresponding increase or decrease in the apoptotic rate of skeletal muscle cells, exhibiting statistically significant differences (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). This implies that the EGR1 gene plays a dual role in not only regulating the inflammatory response in arthritis but also influencing the process of apoptosis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eKOA is a prevalent musculoskeletal disorder that significantly impacts affected individuals through severe knee pain, stiffness, and dysfunction, ultimately leading to disability [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. TKA is an effective intervention for advanced osteoarthritis that can improve patients\u0026rsquo; pain and quality of life. However, postoperative muscle atrophy, mainly quadriceps atrophy, results in reduced muscle strength and mobility that diminishes the benefits of TKA [\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Quadriceps weakness is a contributing factor to long-term postoperative disability assessment in KOA patients [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Patients with post-KOA are known to have muscle weakness and atrophy lasting several years [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Therefore, quadriceps weakness and atrophy concern clinicians treating KOA patients [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Previous research attributed quadriceps atrophy to disuse atrophy caused by joint pain [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], yet studies have proposed that it is a predisposing factor for developing KOA [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Moreover, surgery-induced skeletal muscle trauma is suggested as another potential cause of muscle atrophy [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. However, the biological pathogenesis and underlying mechanisms to develop muscle atrophy in patients who undergo KOA surgery remain unclear.\u003c/p\u003e \u003cp\u003eOur study aimed to identify critical genes involved in pathogenesis mechanisms underlying muscle atrophy. Two GEO datasets were analyzed using bioinformatics to screen for DEGs in both osteoarthritis and muscle atrophy groups systematically. The GSEA demonstrated that inflammatory-related pathways like EMT, TNF-α signaling via NF-κB, and inflammatory response were activated in both osteoarthritis and muscle atrophy disease states. We intersected DEGs in both datasets to obtain 138 common DEGs, which helped us identify genes jointly involved in these pathways. TF enrichment analysis was executed on the 138 common DEGs, and 16 transcription factors were found significantly enriched in osteoarthritis and muscle atrophy. Consequently, we identified EGR1, which regulates the most common DEGs, as the target transcription factor to investigate further pathways of muscle atrophy pathogenesis.\u003c/p\u003e \u003cp\u003eEGR1 is a multifunctional and critical TF involved in various physiological processes, including cell growth, differentiation, and apoptosis [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. EGR1 spans approximately 3.8 Kb containing two exons and an intron. It contains a highly-conserved DNA structural domain of three Cys2-His2 type zinc finger structures on chromosome 18 in mice and chromosome 5 in humans [\u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. EGR1 is predominantly expressed in different connective tissues, such as tendons, cartilage, and bone, regulating extracellular matrix functions to facilitate tissue development, homeostasis, and healing processes [\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Numerous studies have investigated EGR1's role in cartilage and bone, where it contributes to chronic diseases of articular cartilage degeneration [\u003cspan additionalcitationids=\"CR33\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. EGR1 is highly expressed in synovial tissues, and articular cartilage of patients with osteoarthritis [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Under interleukin-1β (IL-1) stimulation, EGR1 recruitment to the Pparg promoter downregulates PPAR expression, restraining its protective role in osteoarthritis [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. In patients with osteoarthritis, excess EGR1 was reported to increase proteins and transcripts of type I collagen in synovial fibroblasts [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. While the effects of EGR1 on human skeletal muscle regulation remain unclear, previous studies highlighted the role of EGR1 in the promotion of differentiation of bovine skeletal muscle-derived satellite cells [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo examine the mechanisms of EGR1-mediated gene regulation, we constructed a PPI network encoded by 90 DEGs. We employed the CytoHubba plug-in to identify hub genes that were predominantly EGR1, FOS, FOSB, KLF2, and JUNB. The Fos gene family includes four members, including FOS, FOSB, FOSL1, and FOSL2, and regulates cellular proliferation, differentiation, and transformation [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. JUNB is a protein-encoded gene located in the nucleoplasm that positively modulates RNA polymerase II transcription [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. It forms heterodimers with FOS family proteins to create the AP-1 transcription complex, enhancing DNA-binding activity, transcriptional activity, and AP-1 consensus sequence specificity [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. The AP-1 complex has been identified to facilitate the pathogenesis of osteoarthritis by binding to the promoter of inflammatory cytokines [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. KLF2 encodes a zinc finger protein within the Kruppel family that is expressed during early mammalian development and functions in multiple growth and disease-related processes, such as adipogenesis, embryonic erythroid cell generation, epithelial cell integrity, inflammation, and T cell viability [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. KLF2 is involved in osteoarthritis progression by regulating matrix metalloproteinases (MMPs), as reported by Takashi Aki et al. [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFinally, we explored the biological functions of EGR1 in the GTEx database. GSEA analysis revealed significant activation of inflammatory-related pathways, including EMT, TNF-α signaling via NF-κB, and the inflammatory response. Notably, the GSEA analysis of the osteoarthritis and muscle atrophy datasets included these three pathways. Therefore, EGR1 affects the process of muscle atrophy in osteoarthritis as it participates in these three pathways.\u003c/p\u003e \u003cp\u003eThrough experimentation, the involvement of the EGR1 gene in osteoarthritis and muscle atrophy has been effectively validated. Firstly, the direct regulatory influence of the TF EGR1 on the FOS, FOSB, KLF2, and JUNB genes was confirmed using real-time fluorescence quantitative polymerase chain reaction. Secondly, the vital role of the EGR1 gene in inflammation regulation was substantiated through an enzyme-linked immunosorbent assay. This regulatory mechanism has been observed to impact various inflammatory pathways, such as EMT, TNF-α signaling via NF-κB, and the inflammatory response, significantly altering the expression of proteins associated with inflammation. Lastly, the regulatory significance of the EGR1 gene in skeletal muscle apoptosis was affirmed through a flow cytometry assay. These experimental findings are consistent with the outcomes of bioinformatics analysis.\u003c/p\u003e \u003cp\u003eIn this study, we employed bioinformatics analysis to determine EGR1 as a key gene implicated in the muscle atrophy observed in patients with KOA. Nevertheless, there are still some limitations in the study design. Firstly, the possibility of bias should be acknowledged because of the small sample size utilized in the bioinformatics analysis. Secondly, further validation through in vivo and in vitro experiments is required. Nonetheless, current technology and capabilities only allow for the exploration of gene overexpression and knockdown via animal experiments, which may introduce some degree of error in comparison to studies involving humans. Additionally, confirmation of the results through collection of clinical samples is essential. Finally, a comprehensive understanding of the main targets is necessary to design effective treatment strategies for post-arthritic muscle atrophy resulting from knee osteoarthritis.\u003c/p\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eThe high expression of the transcription factor EGR1 has been established as a direct regulator of FOS, FOSB, KLF2, and JUNB. This regulatory mechanism has been found to affect various inflammatory pathways, such as EMT, TNF-α signaling via NF-κB, and inflammatory response. These pathways, in turn, have an impact on the postoperative muscle atrophy process in patients with KOA.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eDEG: differentially expressed gene; EGR1: early growth response gene-1; ELISA: enzyme-linked immunosorbent assay; EMT: epithelial mesenchymal transition; FC: fold change; FDR: false discovery rate; GEO: Gene Expression Omnibus database; GO: gene ontology; GO: BP: gene ontology biological process; GSEA: gene set enrichment analysis; IL-1β: Interleukin-1β; KOA: knee Osteoarthritis; MCC: Maximum Clique Centrality; NES: normalization enrichment score; PI: propidium iodide; PPI: protein-protein interaction network; qRT-PCR: reverse transcriptase real-time quantitative polymerase chain reaction; SD: Standard deviation; TF: transcription factor; TNF-α: tumor necrosis factor α; TKA: total knee arthroplasty;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the data used and analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by 1·3·5 project for disciplines of excellence, West China Hospital, Sichuan University (grant No: 2023HXFH012, ZYGD23033). Funds were used to cover experimental reagents and consumables.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors state that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eSilva JMS, Alabarse PVG, Teixeira VON, Freitas EC, de Oliveira FH, Chakr R, Xavier RM: \u003cstrong\u003eMuscle wasting in osteoarthritis model induced by anterior cruciate ligament transection\u003c/strong\u003e. \u003cem\u003ePloS one\u0026nbsp;\u003c/em\u003e2018, \u003cstrong\u003e13\u003c/strong\u003e(4):e0196682.\u003c/li\u003e\n \u003cli\u003eFranz A, Ji S, Bittersohl B, Zilkens C, Behringer M: \u003cstrong\u003eImpact of a Six-Week Prehabilitation With Blood-Flow Restriction Training on Pre- and Postoperative Skeletal Muscle Mass and Strength in Patients Receiving Primary Total Knee Arthroplasty\u003c/strong\u003e. \u003cem\u003eFrontiers in physiology\u0026nbsp;\u003c/em\u003e2022, \u003cstrong\u003e13\u003c/strong\u003e:881484.\u003c/li\u003e\n \u003cli\u003eEthgen O, Bruy\u0026egrave;re O, Richy F, Dardennes C, Reginster JY: \u003cstrong\u003eHealth-related quality of life in total hip and total knee arthroplasty. 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","lastPublishedDoi":"10.21203/rs.3.rs-4839822/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4839822/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eMuscle atrophy is a typical affliction in patients affected by knee Osteoarthritis (KOA). This study aimed to examine the potential pathogenesis and biomarkers that coalesce to induce muscle atrophy, primarily through the utilization of bioinformatics analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e Two distinct public datasets of osteoarthritis and muscle atrophy (GSE82107 and GSE205431) were subjected to differential gene expression analysis and gene set enrichment analysis (GSEA) to probe for common differentially expressed genes (DEGs) and conduct transcription factor (TF) enrichment analysis from such genes. Venn diagrams were used to identify the target TF, followed by the construction of a protein-protein interaction (PPI) network of the common DEGs governed by the target TF. Hub genes were determined through the CytoHubba plug-in whilst their biological functions were assessed using GSEA analysis in the GTEx database. To validate the study, reverse transcriptase real-time quantitative polymerase chain reaction (qRT-PCR), enzyme-linked immunosorbent assay (ELISA), and Flow Cytometry techniques were employed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eA total of 138 common DEGs of osteoarthritis and muscle atrophy were identified, with 16 TFs exhibiting notable expression patterns in both datasets. Venn diagram analysis identified early growth response gene-1 (EGR1) as the target TF, enriched in critical pathways such as epithelial mesenchymal transition, tumor necrosis factor-alpha signaling NF-κB, and inflammatory response. PPI analysis revealed five hub genes, including EGR1, FOS, FOSB, KLF2, and JUNB. The reliability of EGR1 was confirmed by validation testing, corroborating bioinformatics analysis trends.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eEGR1, FOS, FOSB, KLF2, and JUNB are intricately involved in muscle atrophy development. High EGR1 expression directly regulated these hub genes, significantly influencing postoperative muscle atrophy progression in KOA patients.\u003c/p\u003e","manuscriptTitle":"High expression of transcription factor EGR1 is associated with postoperative muscle atrophy in patients with knee osteoarthritis undergoing total knee arthroplasty","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-12 12:18:26","doi":"10.21203/rs.3.rs-4839822/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-09-03T11:46:35+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-31T22:39:44+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-31T15:48:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"63047712006535542649699461573542383041","date":"2024-08-10T05:58:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"228151678271016659150379550579550611616","date":"2024-08-10T05:43:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"63822760525513934499744479480413929892","date":"2024-08-10T02:30:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"217605697977480418448676326915145882676","date":"2024-08-09T11:54:46+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-08-07T09:57:23+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-08-06T07:15:38+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-08-06T06:30:08+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Orthopaedic Surgery and Research","date":"2024-08-01T06:42:37+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-orthopaedic-surgery-and-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"josr","sideBox":"Learn more about [Journal of Orthopaedic Surgery and Research](http://josr-online.biomedcentral.com)","snPcode":"13018","submissionUrl":"https://submission.nature.com/new-submission/13018/3","title":"Journal of Orthopaedic Surgery and Research","twitterHandle":"@MSKmedBMC","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5e194e5d-31d2-4337-97fd-30aa2b287654","owner":[],"postedDate":"September 12th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-10-07T16:00:45+00:00","versionOfRecord":{"articleIdentity":"rs-4839822","link":"https://doi.org/10.1186/s13018-024-05109-9","journal":{"identity":"journal-of-orthopaedic-surgery-and-research","isVorOnly":false,"title":"Journal of Orthopaedic Surgery and Research"},"publishedOn":"2024-10-01 15:57:18","publishedOnDateReadable":"October 1st, 2024"},"versionCreatedAt":"2024-09-12 12:18:26","video":"","vorDoi":"10.1186/s13018-024-05109-9","vorDoiUrl":"https://doi.org/10.1186/s13018-024-05109-9","workflowStages":[]},"version":"v1","identity":"rs-4839822","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4839822","identity":"rs-4839822","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}